基于眼动数据的网络地图界面用户兴趣分析方法

分析网络地图界面用户兴趣对完善网络地图界面设计和提升网络地图用户体验十分重要。现有的分析方法大多针对人机交互数据进行研究,无法直接反映用户的真实兴趣。为解决此问题,该文提出了基于眼动数据的网络地图界面用户兴趣分析方法。首先通过眼动实验,获取了用户在网络地图兴趣区内的注视点数量,并利用熵权法计算用户兴趣度;其次,融入用户背景信息,建立了网络地图用户兴趣信息集,通过决策树ID3算法对其进行分类,归纳提取了用户兴趣分析结果集;最后利用查全率、查准率与F值进行分析结果评价。实验表明,分析结果集匹配成功率为90%,基于眼动数据的用户兴趣分析成功率为85%,高于基于Web日志的用户兴趣分析结果,证明了该方法能有效分析网络地图界面用户兴趣。     

基于众源地理数据的个性化路线服务框架及应用实验

随着移动通信与LBS的蓬勃发展,能够描述个体行为的众源时空大数据大量涌现,为感知群体时空行为模式与探究个性化路线提供了新视角。该文将众源时空信息与出行者的个人意愿映射到实际路网空间,融合大众偏好和定制趋势,构建包含主题序列生成、POI推荐、历史路线推荐的局部路网模型,进而实现一种利用众源时空数据改进的HMM路线规划方法,为用户提供合适且个性化的出行方案;以长沙市岳麓区为研究案例,利用真实路网数据与相关兴趣点作为实验数据,基于该方法可在短时间内提供满足用户需求的不同月份的最优路线。     

基于粗集、遗传神经网络的环境质量评价

基于粗集、遗传神经网络的环境质量评价方法利用粗集对属性的归约功能将数据库中的数据进行归约，并将归约后的数据作为训练数据提供给BP神经网络；再用遗传算法和BP算法相结合的混合算法来训练网络预测模型的结构（在得到最优网络结构的同时也得到网络的最优权值和阈值）。通过粗糙集归约，提高了训练数据表达的清晰度，也减小了BP神经网络的规模，同时利用BP神经网络又克服了粗糙集对噪声数据敏感的影响。这一算法克服了BP算法收敛速度慢、易陷入局部极小等缺陷，实例证明提高了预测精度。     

分布式多标准地学元数据共享研究与实践

多种不同地学元数据标准共存是目前地学数据共享工作中面临的现状之一。以XML及相关内容为基础,实现对多标准地学元数据的一致化定义和表达。通过分析XML元数据在不同处理阶段的形式和特点,提出XML元数据的存储、解析和转换方案。"XML元数据 关键信息项字段映射"方案在保证检索性能的前提下,实现对不同标准元数据的统一存储和管理。在元数据的审查、发布流程中,通过行业数据专家和平台管理员的参与,尽可能确保元数据内容的正确性。通过结合多标准地学元数据共享平台和Web Services等分布式技术,提出星形分布式元数据共享体系;介绍基于该体系构建的地球系统科学数据共享网二期平台的元数据管理实例,目前该体系包括1个总中心和全国范围内的13个分中心。     

一种考虑时间成本的旅游线路推荐方法

旅游行为受时间、空间等方面的限制,当用户有个性化需求,尤其在游览时间方面有特殊要求时,难以快速找到出行线路。个人时空可达性研究在时间、空间制约条件下个人活动的自由度问题,因此向用户推荐旅游线路的问题可转化为在考虑用户偏好的前提下评价时空可达性,向用户推荐可达性好的景点组合,并规划移动路线的问题。该文基于时间地理学理论,提出了一种考虑游客时间成本的旅游线路推荐算法,使用时空可达性评价景点,计算可达性好的景点组合,结合地理信息科学中的路径分析理论,规划旅游线路。算法的验证实验在Web端进行,通过设置不同场景,对每种情况下的推荐结果进行了分析与展示。结果表明,该文提出的算法具有可行性与合理性,能满足用户在游览时间、景点类型方面的需求,同时佐证了时间地理学在旅游线路推荐研究中的可行性与科学性。     

地学数据集成的理论基础与集成体系

地球空间数据 (简称地学数据 )来源的拓宽、更新手段的发展和应用领域的扩大使数据集成或集成使用的研究和实用化成为必需。简单地理解 ,地学数据集成是指不同来源、不同性状数据在相同环境下的使用。地学数据是对地理现象和过程及过程时空特征认知基础上的表达 ,地学数据集成的基础主要表现在 :地理现象和过程的空间和时间统一性、地学过程时空过程的连续性、地学现象和过程的层次性、地学数据认知的一致性、依赖于元数据的地学数据的透明性、数据内容和形式的相对独立性等 ;在此基础上 ,作者在论文中描述了基于地学知识和地理信息系统功能的地学数据集成概念模型和过程 ,并对地学数据集成过程中涉及到的问题进行了说明。     

草原高时空分辨率NDVI重构模型的精度分析

多源遥感影像时空信息融合产生的高时空分辨率影像是遥感地学应用的重要数据源,利用时空融合算法形成的高时空分辨率植被指数数据集对于植被动态监测具有重要意义。为了在草原植被监测中更好地应用时空融合算法,该文利用2013-2016年间内蒙古自治区呼伦贝尔和锡林郭勒地区多期Landsat8和MODIS影像进行高时空分辨率NDVI植被指数计算,定量评价了STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)、CDSTARFM(Combination of Downscaling Mixed Pixel Algorithm and Spatial and Temporal Adaptive Reflectance Fusion Model)和STDFM(Spatial and Temporal Data Fusion Model)3种常用时空融合算法在不同融合策略下的精度。研究结果表明,基于NDVI融合策略的STARFM算法更适合草原地区高时空分辨率NDVI数据的构建。     

室内定位数据分析与应用研究进展

现代人文地理学的研究越来越多地关注人的时空行为,而获取个体在出行活动中的时空位置数据是研究人类时空行为的前提。受数据获取技术的限制,之前对时空行为的研究主要集中在室外空间。随着室内定位技术的出现和应用,这类研究由室外空间扩展至室内空间。室内定位技术和方法较多,但从数据的角度来看,根据数据获取中使用定位方法的不同,可将室内定位数据分为几何位置数据、指纹位置数据和符号位置数据3类。目前,基于室内定位数据的研究可以归结为以下4个方面,即：人在室内的时空分布、人在室内的移动模式、人在室内的行为习惯及属性推断、人与室内环境的交互作用。然而,总体上目前的研究还处于探索阶段,理论和方法体系尚未成熟。本文认为后续的研究中需要关注以下问题：①数据获取方面。相对于蓝牙、射频识别、红外等定位技术,“智能手机+WiFi”模式的定位系统具有覆盖范围广、成本低廉、无需专门设备支持、易与用户交互等优势,是一种最具应用前景的室内定位技术;②研究内容方面。时空行为特征的研究是基础,个体属性推断及个体与环境的相互作用形式和机理研究将是重点,多时空尺度数据融合分析是一种趋势;③科学伦理方面。室内定位涉及微观尺度人类活动的记录,隐私保护问题需要高度关注。     

连续证据权重法及其在矿产潜力预测中的应用研究

目前分类证据权重法只能处理分类证据因子,连续证据因子在转化为分类数据时,必然导致信息损失;而且证据因子按分类计算权重,不能从整体上反映该证据因子对成矿有利的权重。该文尝试对分类证据权重法进行改进,将分类证据隶属度扩充为连续证据隶属度,并修改证据因子权重的计算方法,避免同一证据因子权重的重复累加,建立基于连续证据因子的模糊证据权重法。以实际的矿产潜力预测为例,对比分析分类证据权重法与所提出的模糊连续证据权重法。结果表明,基于连续证据因子的模糊证据权重法能够克服分类证据模型后验概率空间突变情况,利于预测结果的制图输出,并在一定程度上提高后验概率精度。     

基于隐马尔可夫模型的时空轨迹语义匹配方法

时空轨迹数据关联的语义信息能更好地反映用户行为,对于POI密集分布的城市区域,轨迹的语义信息很难根据单一的距离或时间要素进行匹配,该文设计一种基于隐马尔可夫模型(HMM)的时空轨迹语义匹配方法。首先,利用时间阈值与距离阈值提取逗留点,并利用考虑时间的DBSCAN聚类方法对逗留点进行聚类,得到由抽象停留位置构成的轨迹;然后,结合POI数据获得停留位置的候选语义,再以停留位置序列为观测序列,以每个停留位置所关联的候选地点作为隐藏状态建立HMM,并用改进的加权距离的TF-IDF方法计算HMM的观测概率;最后,解算HMM得到最有可能的访问地点序列作为轨迹的语义匹配结果。该方法不依赖其他外部数据或训练数据,适用于POI密集分布的城市区域,基于真实时空轨迹数据集的实验结果验证了该方法的有效性。     

地理学时空数据分析方法

随着地理空间观测数据的多年积累,地球环境、社会和健康数据监测能力的增强,地理信息系统和计算机网络的发展,时空数据集大量生成,时空数据分析实践呈现快速增长。本文对此进行了分析和归纳,总结了时空数据分析的7类主要方法,包括：时空数据可视化,目的是通过视觉启发假设和选择分析模型;空间统计指标的时序分析,反映空间格局随时间变化;时空变化指标,体现时空变化的综合统计量;时空格局和异常探测,揭示时空过程的不变和变化部分;时空插值,以获得未抽样点的数值;时空回归,建立因变量和解释变量之间的统计关系;时空过程建模,建立时空过程的机理数学模型;时空演化树,利用空间数据重建时空演化路径。通过简述这些方法的基本原理、输入输出、适用条件以及软件实现,为时空数据分析提供工具和方法手段。     

Fine-grained prediction of urban population using mobile phone location data

Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.     

Evaluation of inductive logic programming for information extraction from natural language texts to support spatial data recommendation services

In this article we analyze a well-known and extensively researched problem: how to find all datasets, on the one hand, and on the other hand only those that are of value to the user when dealing with a specific spatially oriented task. In analogy with existing approaches to a similar problem from other fields of human endeavor, we call this software solution ‘a spatial data recommendation service.’ In its final version, this service should be capable of matching requests created in the user's mind with the content of the existing datasets, while taking into account the user's preferences obtained from the user's previous use of the service. As a result, the service should recommend a list of datasets best suited to the user's needs. In this regard, we consider metadata, particularly natural language definitions of spatial entities, a crucial piece of the solution. To be able to use this information in the process of matching the user's request with the dataset content, this information must be semantically preprocessed. To automate this task we have applied a machine learning approach. With inductive logic programming (ILP) our system learns rules that identify and extract values for the five most frequent relations/properties found in Slovene natural language definitions of spatial entities. The initially established quality criterion for identifying and extracting information was met in three out of five examples. Therefore we conclude that ILP offers a promising approach to developing an information extraction component of a spatial data recommendation service.     

A lightweight ensemble spatiotemporal interpolation model for geospatial data

ABSTRACT

Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations.     

A spatiotemporal indexing approach for efficient processing of big array-based climate data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (~10× speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328%). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.     

A model to predict ordinal suitability using sparse and uncertain data

We describe the development of the algorithms that comprise the Spatial Decision Support System (SDSS) CaNaSTA (Crop Niche Selection in Tropical Agriculture). The system was designed to assist farmers and agricultural advisors in the tropics to make crop suitability decisions. These decisions are frequently made in highly diverse biophysical and socioeconomic environments and must often rely on sparse datasets.The field trial datasets that provide a knowledge base for SDSS such as this are characterised by ordinal response variables. Our approach has been to apply Bayes’ formula as a prediction model.This paper does not describe the entire CaNaSTA system, but rather concentrates on the algorithm of the central prediction model. The algorithm is tested using a simulated dataset to compare results with ordinal regression, and to test the stability of the model with increasingly sparse calibration data. For all but the richest input datasets it outperforms ordinal regression, as determined using Cohen’s weighted kappa. The model also performs well with sparse datasets. Whilst this is not as conclusive as testing with real world data, the results are encouraging.     

Leveraging correlation across space and time to interpolate geophysical data via CoKriging

Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time).